adversarial
Llama-3.1-8B-Instruct_Anti-Adversarialadversarial-paraphraser-qwen3-8b-GGUFadversarial-paraphrasing-detectorroberta-base-finetune-adversarial-qaElectra-base-squad-adversarialqa-epoch-3rloo_pythia410m_tldr6.9b_rm410mdatarloo_pythia410m_tldr6.9b_rm410mdata_propsft_propprefix_noklflan_t5_large-adversarial_qa_dbert_question_context_answer
adversarial_qa
Dataset Card for adversarialQA
Dataset Summary
We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial human… See the full description on the dataset page: https://huggingface.co/datasets/UCLNLP/adversarial_qa.mnist-adversarial-datasetviyog-adversarial
Viyog — adversarial samples
Precomputed adversarial examples used in Viyog. Attacks: FGSM, BIM, PGD,
APGD-CE (full), plus DeepFool and CW (capped) — crafted against the finetuned
backbones in amanyagami/viyog-weights.
Format — HDF5 (<model>_<attack>.h5): images (uint8, NCHW 224x224) + labels (int32).
root: CIFAR-100 attacks · cifar10/: CIFAR-10 attacks
Load with h5py. Package: pip install viyog · code: https://github.com/amanyagami/viyog
squad_adversarialHere are two different adversaries, each of which uses a different procedure to pick the sentence it adds to the paragraph:
AddSent: Generates up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. Picks the one that most confuses the model.
AddOneSent: Similar to AddSent, but just picks one of the candidate sentences at random. This adversary is does not query the model in any way.ImageNet-Adversarialstandard_chat_manage_tabs_adversarial